MDF is a conserved splicing factor and modulates cell division and stress response in Arabidopsis

The Arabidopsis splicing factor PORCUPINE/SmE1 orchestrates temperature-dependent root development via auxin homeostasis maintenance

Appropriate abiotic stress response is pivotal for plant survival and makes use of multiple signaling molecules and phytohormones to achieve specific and fast molecular adjustments. A multitude of studies has highlighted the role of alternative splicing in response to abiotic stress, including temperature, emphasizing the role of transcriptional regulation for stress response. Here we investigated the role of the core-splicing factor PORCUPINE (PCP) on temperature-dependent root development. We used marker lines and transcriptomic analyses to study the expression profiles of meristematic regulators and mitotic markers, and chemical treatments, as well as root hormone profiling to assess the effect of auxin signaling. The loss of PCP significantly alters RAM architecture in a temperature-dependent manner. Our results indicate that PCP modulates the expression of central meristematic regulators and is required to maintain appropriate levels of auxin in the RAM. We conclude that alternative pre-mRNA splicing is sensitive to moderate temperature fluctuations and contributes to root meristem maintenance, possibly through the regulation of phytohormone homeostasis and meristematic activity.

SCI1, a flower regulator of cell proliferation, and its partners NtCDKG2 and NtRH35 interact with the splicing machinery

Successful plant reproduction depends on the adequate development of floral organs controlled by cell proliferation and other processes. The Stigma/style cell-cycle inhibitor 1 (SCI1) gene regulates cell proliferation and affects the final size of the female reproductive organ. To unravel the molecular mechanism exerted by Nicotiana tabacum SCI1 in cell proliferation control, we searched for its interaction partners through semi-in vivo pull-down experiments, uncovering a cyclin-dependent kinase, NtCDKG;2. Bimolecular fluorescence complementation and co-localization experiments showed that SCI1 interacts with NtCDKG;2 and its cognate NtCyclin L in nucleoli and splicing speckles. The screening of a yeast two-hybrid cDNA library using SCI1 as bait revealed a novel DEAD-box RNA helicase (NtRH35). Interaction between the NtCDKG;2-NtCyclin L complex and NtRH35 is also shown. Subcellular localization experiments showed that SCI1, NtRH35, and the NtCDKG;2-NtCyclin L complex associate with each other within splicing speckles. The yeast two-hybrid screening of NtCDKG;2 and NtRH35 identified the conserved spliceosome components U2a', NF-κB activating protein (NKAP), and CACTIN. This work presents SCI1 and its interactors, the NtCDKG;2-NtCyclin L complex and NtRH35, as new spliceosome-associated proteins. Our findings reveal a network of interactions and indicate that SCI1 may regulate cell proliferation through the splicing process, providing new insights into the intricate molecular pathways governing plant development.

Estimating genome-wide DNA methylation heterogeneity with methylation patterns

BACKGROUND

In a heterogeneous population of cells, individual cells can behave differently and respond variably to the environment. This cellular diversity can be assessed by measuring DNA methylation patterns. The loci with variable methylation patterns are informative of cellular heterogeneity and may serve as biomarkers of diseases and developmental progression. Cell-to-cell methylation heterogeneity can be evaluated through single-cell methylomes or computational techniques for pooled cells. However, the feasibility and performance of these approaches to precisely estimate methylation heterogeneity require further assessment.

RESULTS

Here, we proposed model-based methods adopted from a mathematical framework originally from biodiversity, to estimate genome-wide DNA methylation heterogeneity. We evaluated the performance of our models and the existing methods with feature comparison, and tested on both synthetic datasets and real data. Overall, our methods have demonstrated advantages over others because of their better correlation with the actual heterogeneity. We also demonstrated that methylation heterogeneity offers an additional layer of biological information distinct from the conventional methylation level. In the case studies, we showed that distinct profiles of methylation heterogeneity in CG and non-CG methylation can predict the regulatory roles between genomic elements in Arabidopsis. This opens up a new direction for plant epigenomics. Finally, we demonstrated that our score might be able to identify loci in human cancer samples as putative biomarkers for early cancer detection.

CONCLUSIONS

We adopted the mathematical framework from biodiversity into three model-based methods for analyzing genome-wide DNA methylation heterogeneity to monitor cellular heterogeneity. Our methods, namely MeH, have been implemented, evaluated with existing methods, and are open to the research community.